A short description of the post.
Since late December 2019, an outbreak of a novel coronavirus disease (COVID-19; previously known as 2019-nCoV) was reported in Wuhan, China, which had subsequently affected 210 countries worldwide. In general, COVID-19 is an acute resolved disease but it can also be deadly, with a 2% case fatality rate.
The COVID-19 pandemic in Indonesia is part of the ongoing worldwide pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus was confirmed to have reached Indonesia on March 2, 2020. It started with two cases in March. As of July 31 2021, there had been 3,409,658 cumulative confirmed cases of COVID-19 in Indonesia and 94,119 reported cumulative deaths. All cases were spread in 34 provinces in Indonesia. Among all the provinces, DKI Jakarta (Indonesian: Daerah Khusus Ibukota Jakarta and in English: Special Capital Region of Jakarta) contributed close to 24% of the cumulative confirmed cases.
Despite its compactness, the cumulative confirmed cases were not evenly distributed within DKI Jakarta as Figure below reveals at the district and sub-district levels. The question was where were the sub-districts with relatively higher number of confirmed cases and how they changed over time.
Goals: - Identify the sub-districts with higher number of confirmed cases than average - Visualize the change in COVID-19 infection from March 2020 to August 2021
Data: 1. Open Data Covid-19 Provinsi DKI Jakarta - source: Riwayat File Covid-19 DKI Jakarta - Format: xlsx 2. Shapefile (SHP) Batas Desa Provinsi DKI Jakarta - source: PODES 2019 - Format: shp
Packages used: maptools, sf, raster, spatstat and tmap, tidyverse, readxl
packages = c('maptools', 'sf', 'raster','spatstat', 'tmap','tidyverse','readxl')
for(p in packages){
if(!require(p,character.only = T)){
install.packages(p)
}
library(p,character.only = T)
}
Reading the datas
jakarta = st_read(dsn = "data/Geospatial",
layer = "BATAS_DESA_DESEMBER_2019_DUKCAPIL_DKI_JAKARTA")
Reading layer `BATAS_DESA_DESEMBER_2019_DUKCAPIL_DKI_JAKARTA' from data source `C:\JunLonggggg\junlong-is415\_posts\2021-09-06-take-home-exercise-1\data\Geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 269 features and 161 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 106.3831 ymin: -6.370815 xmax: 106.9728 ymax: -5.184322
Geodetic CRS: WGS 84Check crs
st_crs(jakarta)
Coordinate Reference System:
User input: WGS 84
wkt:
GEOGCRS["WGS 84",
DATUM["World Geodetic System 1984",
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
CS[ellipsoidal,2],
AXIS["latitude",north,
ORDER[1],
ANGLEUNIT["degree",0.0174532925199433]],
AXIS["longitude",east,
ORDER[2],
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4326]]Upon Reading the CRS, we can see that the projected coordinate systrem is different from the designated system of Indoneisa, which is DGN95 / Indonesia TM-3 zone 54.1, EPSG: 23845.
We then have to transform the projection to EPSG:23845.
jakarta_sf23845 = st_transform(jakarta, 23845)
st_crs(jakarta_sf23845)
Coordinate Reference System:
User input: EPSG:23845
wkt:
PROJCRS["DGN95 / Indonesia TM-3 zone 54.1",
BASEGEOGCRS["DGN95",
DATUM["Datum Geodesi Nasional 1995",
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4755]],
CONVERSION["Indonesia TM-3 zone 54.1",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",0,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",139.5,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",0.9999,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",200000,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",1500000,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["easting (X)",east,
ORDER[1],
LENGTHUNIT["metre",1]],
AXIS["northing (Y)",north,
ORDER[2],
LENGTHUNIT["metre",1]],
USAGE[
SCOPE["Cadastre."],
AREA["Indonesia - onshore east of 138°E."],
BBOX[-9.19,138,-1.49,141.01]],
ID["EPSG",23845]]The change has been completed. WE can use st_geometry to take a look at the sf attributes.
st_geometry(jakarta_sf23845)
Geometry set for 269 features
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -3691981 ymin: 663887.8 xmax: -3606237 ymax: 815440.9
Projected CRS: DGN95 / Indonesia TM-3 zone 54.1
First 5 geometries:Finding the common values of outer islands. By clicking at a few of the outer islands, we can see that one common data value is “KEPULAUAN SERIBU” under “KAB_KOTA”(City District) attribute.
tmap_mode("view")
tm_shape(jakarta_sf23845)+
tm_polygons()
We can add in colors to confirm that the outer islands have a common value for KAB_KOTA
tm_shape(jakarta_sf23845)+
tm_polygons() +
tm_shape(jakarta_sf23845) +
tm_fill("KAB_KOTA",
palette = "RdYlBu")+
tm_borders()